Technology integration and its role in promoting green sustainable campuses in Saudi Arabia

In recent years, green sustainable campuses have gained increasing attention as a crucial aspect of environmental sustainability (Alalawi and Omar, 2024), particularly in higher education institutions worldwide (Moghayedi et al., 2024). Universities, as centers of knowledge and innovation, play a fundamental role in driving sustainable development practices (Shishakly et al., 2024), especially through the integration of advanced technologies to enhance environmental efficiency (Irfan et al., 2024). Institutions in North America, Europe and Asia have made substantial progress in implementing sustainable campus initiatives. For example, universities in the USA and Canada have incorporated smart technologies, energy-efficient infrastructure and Internet of Things (IoT)-based systems to optimize resource utilization (Turner et al., 2023). In Europe, institutions have adopted circular economy strategies, leveraging data analytics to enhance energy conservation and reduce waste (Müller et al., 2024). Similarly, in Asia, rapid technological advancements and government-driven sustainability policies have encouraged universities to adopt automation, IoT and smart grid systems for campus sustainability (Chen et al., 2024). Saudi Arabian universities, driven by Saudi Vision 2030, are aligning their strategies with global sustainability trends, integrating various technologies to promote environmental efficiency (Alshehri et al., 2023). However, despite these efforts, research remains limited on how specific technological elements contribute to achieving green sustainable campuses, particularly in the context of emerging economies (Al-Ghamdi et al., 2024). This study aims to fill this gap by investigating the role of technology integration in fostering green sustainable campuses in Saudi Arabia, focusing on SBT, IoT systems (IoTS), DARM and AF. These technologies possess the potential to transform university campuses into energy-efficient, resource-conscious environments that align with broader sustainability objectives (Tetteh et al., 2024; Raman et al., 2024). However, empirical research is needed to examine how technology adoption facilitates these advancements and how DI either strengthens or restricts sustainability outcomes (El-Farra et al., 2023).

Drawing on the DOI, this study asserts that universities adopt innovative technologies through systematic patterns influenced by both internal and external factors (Rogers, 2003). DOI highlights that the adoption of an innovation is driven by its relative advantage, complexity and compatibility (Al-Adwani, 2024). Universities worldwide have leveraged these principles in adopting smart energy management systems, IoT-based waste reduction models and artificial intelligence (AI)-driven automation to improve sustainability (Lee et al., 2024; García et al., 2024). In Saudi universities, technologies such as SBT and IoT offer substantial benefits, including improved energy efficiency, optimized resource utilization and reduced carbon footprints (Al-Mutairi et al., 2024). However, successful implementation relies on an effective adoption process, necessitating an in-depth exploration of technology adoption as a mediator (Hassan and Khalil, 2023). Furthermore, DI is crucial in moderating the effectiveness of technology adoption. Strong DI ensures seamless data integration, real-time monitoring and automation (Ahmed et al., 2024a; 2024b). Universities in developed regions benefit from well-established DI, which facilitates the adoption of AI-driven campus management and energy-efficient automation (Gonzalez-Garcia et al., 2023). However, in emerging economies like Saudi Arabia, challenges such as inconsistent network infrastructure and limited digital transformation readiness may hinder the full potential of technology-driven sustainability initiatives (Alharbi et al., 2023). This study examines how DI moderates the impact of SBT, IoTS, DARM and AF on green campus sustainability within this unique socioeconomic and technological context. The alignment of this research with Saudi Vision 2030 and the United Nations Sustainable Development Goals (SDGs) is evident. Vision 2030 emphasizes digital transformation and sustainability as fundamental pillars of national progress (Saudi Vision 2030, 2023). It promotes technology adoption in higher education to enhance resource efficiency, environmental conservation and institutional resilience. By focusing on green sustainable campuses, this study contributes to SDG targets related to clean energy, smart cities and responsible consumption (Al-Saud, 2024). Additionally, it supports quality education by advocating for digitally integrated, eco-conscious learning environments (UN SDGs, 2024). Despite the progress in sustainable campus development, existing literature has primarily examined individual technologies rather than their collective impact and adoption dynamics (Omar et al., 2024). Moreover, limited research has explored the role of DI in facilitating or impeding successful technology integration in higher education (Bin Mughairi et al., 2024). This study aims to bridge these gaps by offering a holistic investigation into how universities can strategically integrate emerging technologies to enhance sustainability outcomes.

This study argues that effective technology integration within Saudi Universities requires both the adoption of innovative technologies and the enhancement of digital infrastructure to support seamless operations. A comparative perspective with global case studies will help highlight how Saudi universities can align with best practices while addressing their unique challenges. The adoption of SBT, IoTS, DARM and AF positively impacts sustainability outcomes, but these effects are significantly enhanced by robust DI and structured adoption strategies (Youssef et al., 2024). The primary objective of this research is to evaluate the role of technology integration in promoting green sustainable campuses in Saudi Arabia, emphasizing TA as a mediator and DI as a moderator. By examining these factors, this study provides valuable insights into leveraging advanced technologies for sustainability, contributing to Saudi Vision 2030 and SDGs. The findings will guide policymakers, educators and technology providers in designing effective strategies for technology adoption and infrastructure development in higher education institutions.

Green sustainable campus (GSC): The extent to which university infrastructure integrates eco-friendly practices, resource efficiency and digital technologies to minimize environmental impact and promote sustainable operations.

Smart building technologies (SBT): Advanced automated systems designed to optimize energy use, lighting, security and environmental conditions within university facilities to enhance sustainability and resource efficiency.

IoT systems (IoTS): Network of interconnected devices and sensors that enable real-time data collection, monitoring and management of campus resources for improved sustainability outcomes.

Data analytics for resource management (DARM): Use of data-driven insights to enhance decision-making, optimize resource consumption and improve energy efficiency across university campuses.

Automation in facilities (AF): Implementation of automated processes in campus facilities to enhance operational efficiency, reduce resource wastage and support sustainable management.

Technology adoption (TA): The process by which universities accept, implement and use new technologies to enhance campus sustainability and operational efficiency.

Digital infrastructure (DI): The availability and quality of digital systems and networks that support technology integration and facilitate sustainable practices in university campuses.

The DOI, established by Everett Rogers (2003), serves as the theoretical foundation for this study, examining how innovations such as SBT, IoTS, DARM and AF contribute to GSC in Saudi Arabia and globally. DOI posits that adoption is influenced by factors such as relative advantage, compatibility, complexity, trialability and observability, making it particularly relevant for understanding technology integration in sustainability initiatives across higher education institutions worldwide. Relative advantage is evident in SBT and IoTS, which improve energy efficiency and operational cost reduction, fostering higher adoption rates and enhancing campus sustainability (Mahmoud et al., 2024; Khan et al., 2023). DARM aligns with compatibility by integrating smoothly with existing data-driven practices, facilitating adoption without significant disruption (Al-Qahtani and Al-Shehri, 2024). While AF introduces complexity, effective TA mitigates challenges through user training and simplified systems (Irfan et al., 2024). Trialability and observability are crucial, as piloting technologies such as SBT and AF allows stakeholders to assess benefits before full-scale implementation, encouraging adoption (Hassan and Khalil, 2023). Beyond technological factors, emerging innovations such as AI-driven automation and renewable energy systems are increasingly shaping sustainable campus development. AI enhances predictive energy management and operational efficiency, while renewable energy solutions such as solar grids and smart energy storage reduce environmental impact and contribute to carbon neutrality (García et al., 2024). Additionally, non-technological factors, such as institutional policies, leadership commitment and sustainability culture, influence adoption rates and long-term effectiveness. DI plays a crucial moderating role, providing the necessary environment for seamless integration and maximizing innovation diffusion (Rahman et al., 2023). Unlike frameworks such as technology–organization–environment, DOI uniquely incorporates both innovation attributes and adoption contexts, making it ideal for exploring sustainability initiatives not only in Saudi universities under Vision 2030 but also within the broader global landscape of higher education sustainability transformations (Saudi Vision 2030, 2023; UN SDGs, 2024).

The adoption of SBT plays a pivotal role in fostering sustainable practices within university campuses, aligning with the principles of the DOI. DOI emphasizes relative advantage, compatibility and observability as key factors driving innovation adoption (Rogers, 2003). SBT, including energy-efficient lighting, automated heating and cooling systems and intelligent security controls, offers clear benefits by reducing energy consumption, enhancing operational efficiency and minimizing carbon footprints, which significantly contribute to the development of GSC (Mahmoud et al., 2024). Furthermore, the compatibility of SBT with existing campus infrastructures facilitates their seamless integration, making them highly attractive to administrators aiming for sustainability transformations (Al-Ghamdi et al., 2024). DOI also underscores the importance of visible outcomes, such as energy savings and operational cost reductions, in increasing adoption rates (Rahman et al., 2023). Empirical evidence demonstrates that universities implementing SBT achieve substantial improvements in sustainability metrics, reinforcing their positive impact on GSC (Al-Qahtani and Al-Shehri, 2024). Thus, the hypothesis proposed is as follows:

The adoption of IoTS serves as a transformative approach to advancing campus sustainability, aligning with Saudi Vision 2030’s goals of creating efficient, technology-driven educational environments (Al-Mutairi et al., 2024). IoTS facilitates real-time data collection, monitoring and resource optimization, enhancing energy efficiency, waste management and water conservation – all critical components of GSC (Mahmoud et al., 2024). By enabling dynamic control over campus operations, such as energy adjustments based on real-time data, IoTS directly supports resource efficiency and operational sustainability (El-Farra et al., 2023). The DOI suggests that technologies like IoTS, which offer clear advantages and observable benefits, are more readily adopted (Rogers, 2003). Furthermore, its seamless integration with existing digital infrastructures enhances its compatibility and adoption within universities (Hassan and Khalil, 2023). Empirical evidence confirms IoTS’s effectiveness in reducing resource wastage, leading to the proposed hypothesis:

DARM plays a critical role in advancing sustainability in universities by offering actionable insights for optimizing energy use, reducing waste and improving resource allocation (Khan et al., 2023). By analyzing real-time resource usage patterns, DARM enables campuses to identify inefficiencies and implement corrective measures, directly contributing to GSC (Al-Qahtani et al., 2024). DOI highlights that innovations demonstrating compatibility and observable benefits are more likely to be adopted (Rogers, 2003). DARM’s alignment with existing digital systems ensures seamless integration into sustainability initiatives, supporting the trend of data-driven decision-making in university operations (Irfan et al., 2024). Additionally, its trialability allows universities to test its effectiveness through pilot implementations, increasing its adoption likelihood (Rahman et al., 2023). Empirical evidence confirms that campuses utilizing DARM achieve superior resource management, supporting the hypothesis:

AF is a critical enabler of sustainability in university campuses, optimizing operations, reducing resource waste and enhancing energy management (Mahmoud et al., 2024). Incorporating systems for automated lighting, heating, cooling and waste management, AF significantly lowers environmental footprints and supports the development of GSC (Al-Qahtani and Al-Shehri, 2024). The DOI emphasizes that innovations offering observable benefits and easy integration are more likely to be adopted (Rogers, 2003). Despite its perceived complexity, AF’s operational efficiency and cost-saving benefits make it an attractive choice for universities prioritizing sustainability (Al-Ghamdi et al., 2024). Additionally, AF’s trialability enables universities to test these technologies in phases, assessing their impact on sustainability before full-scale adoption, aligning with DOI’s principles (Rahman et al., 2023). Research confirms that campuses employing AF achieve significant improvements in energy efficiency and resource optimization, reinforcing the proposed hypothesis:

The integration of SBT into university campuses enhances operational sustainability by optimizing energy use, improving environmental conditions and minimizing waste, all contributing to GSC (Hassan et al., 2024a; 2024b; 2024c; 2024d). However, the full potential of SBT in achieving sustainability outcomes relies on effective TA, which ensures user acceptance, adequate training and alignment with institutional sustainability goals (Al-Qahtani and Al-Shehri, 2024). The DOI emphasizes that successful adoption is influenced by perceived relative advantage, compatibility and simplicity of the innovation (Rogers, 2003). Thus, it is hypothesized as follows:

IoTS contribute to sustainability by enabling real-time monitoring and optimization of resources such as energy, water and waste (Mahmoud et al., 2024). The adoption of IoTS enhances its utility by integrating its features seamlessly into campus operations, encouraging engagement from stakeholders (Al-Adwani et al., 2024). DOI highlights that adoption is driven by observable benefits and compatibility, making TA essential in leveraging the full potential of IoTS for sustainability outcomes (Rogers, 2003). Thus, it is hypothesized:

DARM supports informed decision-making by identifying inefficiencies in energy and resource use, contributing significantly to GSC (Khan et al., 2024). TA ensures the successful adoption of DARM by addressing user readiness and aligning analytics solutions with operational goals (Rahman and Al-Harbi, 2024). According to DOI, visibility of benefits encourages broader adoption, making TA pivotal in maximizing DARM’s impact on sustainability (Rogers, 2003). Thus, it is hypothesized:

AF promotes sustainability by streamlining operations, reducing wastage and improving energy management on campuses (Mahmoud et al., 2024). Effective TA mitigates perceived complexity and facilitates user engagement, making AF integration more impactful (Al-Ghamdi et al., 2024). DOI posits that innovations with trialability and clear benefits are more likely to succeed in complex systems such as university campuses (Rogers, 2003).

Thus, it is hypothesized as follows:

DI plays a critical role in moderating the relationship between innovative technologies – SBT, IoTS, DARM and AF – and GSC. Robust DI provides the foundation for integrating advanced technologies by enabling seamless communication, real-time data exchange and enhanced operational efficiency (Ahmed et al., 2024a; 2024b). For SBT, DI ensures the effective implementation of automated energy systems, lighting and environmental controls, directly enhancing energy efficiency and sustainability outcomes (Al-Qahtani et al., 2024). Similarly, IoTS rely on DI to support sensor networks and facilitate responsive resource consumption adjustments, driving sustainability practices (Mahmoud et al., 2024). DARM depends on DI for processing large data sets and delivering actionable insights, improving decision-making and optimizing resource management strategies for GSC (Al-Mutairi et al., 2024). Likewise, AF benefits from strong DI by streamlining facility operations, reducing wastage and enhancing workflow efficiency, all contributing to sustainability objectives (Rahman and Al-Saadi, 2024). According to the DOI, external factors such as DI significantly influence the adoption and effectiveness of innovations, enhancing compatibility and reducing perceived complexity (Rogers, 2003). Robust DI facilitates the observability of benefits, making technology adoption more seamless and impactful (Irfan et al., 2024). Conversely, weak DI may impede these technologies’ full potential, limiting their contribution to sustainability outcomes (Hassan and Al-Mutairi, 2024). Thus, it is hypothesized as follows:

The conceptual framework demonstrates the relationships between advanced technologies – SBT, IoTS, DARM and AF – and GSC, with TA mediating their effectiveness in achieving sustainability. Additionally, DI moderates these relationships, amplifying the impact of these technologies in universities with strong digital capabilities. Grounded in the DOI, the framework emphasizes how adoption processes and supportive environments enable innovations to drive sustainability outcomes. The proposed conceptual framework has been depicted in Figure 1.

As no existing survey instrument was suitable for the variables in this study, we developed the measurement items specifically for this research. A systematic and rigorous process was followed to ensure the instrument’s validity, reliability and suitability for the study context. Five items were developed for each construct, comprehensively addressing all dimensions. The creation of new items involved brainstorming and in-depth discussions with subject-matter experts, including social scientists, demographers, human resource management specialists, sociologists, sustainable energy consultants, facility managers, environmental policy advisors, environmental scientists, sustainability consultants and technology and sustainability experts. Their feedback refined the items to ensure clarity, relevance and alignment with the construct definitions. Language experts reviewed the questionnaire to remove ambiguities and double-barreled questions, and three academic researchers in sustainability validated the theoretical alignment of the constructs. A five-point Likert scale was employed for consistency across responses. Redundant, unclear or irrelevant items were eliminated, and the revised questionnaire was tested with university students, staff and academics to assess clarity and applicability. Based on their feedback, additional refinements were made. A pilot study involving 120 respondents was conducted, yielding Cronbach’s alpha values that confirmed internal consistency: SBT = 0.920, IoTS = 0.898, DARM = 0.864, AF = 0.927, DI = 0.947, TA = 0.910 and GSC = 0.973, all exceeding the acceptable threshold of 0.7. Exploratory Factor Analysis (EFA) using SPSS v26 further validated the instrument. The Kaiser–Meyer–Olkin (KMO) value was 0.764, Bartlett’s Test was significant (p < 0.000), communalities exceeded 0.4 and seven factors with eigenvalues >1 explained 79.573% of the cumulative variance. The Rotated Component Matrix showed no cross-loadings, confirming the distinctiveness of the constructs. The final questionnaire and the rotated component matrix are included in the supplementary file. These results confirm that the instrument is valid, reliable and robust for application in the main study.

The target population for this study included academic staff, administrative staff and students from the top nine universities in Saudi Arabia, based on their QS rankings, along with the University of Tabuk, where the research originated. The inclusion of the University of Tabuk, ranked between 1,001 and 1,200 in the QS rankings, ensured broader representation. The total population was approximately 220,000 individuals. A sample size of 429 respondents was deemed sufficient for this study. A structured survey questionnaire was meticulously designed to collect data, and a two-step sampling approach was used. First, the top nine universities and the University of Tabuk were selected. Subsequently, convenience sampling was used to recruit participants from faculty, administrative staff and students. Survey links were distributed through relevant departments and administrative offices after obtaining all necessary approvals. Strict protocols were followed to ensure data security and privacy. To enhance response rates, follow-up reminders were sent. The data collection process, conducted between August and September 2024, yielded 448 responses. After thorough screening, 429 complete responses were retained for analysis, while 19 incomplete responses were excluded. The data collected provided a robust foundation for analyzing the relationships among the study variables, enabling meaningful insights into the role of technology integration in promoting green sustainable campuses.

The survey respondents’ demographic variables reveal that 46.62% are male (200 respondents) and 53.38% are female (229 respondents). In terms of age, 37.30% are between 18 and 24 years (160 respondents), 10.72% are between 25 and 34 years (46 respondents), 22.14% are between 35 and 44 years (95 respondents), another 22.14% are between 45 and 54 years (95 respondents) and 7.69% are 55 years or above (33 respondents). Regarding their position, 43.59% are academic staff (187 respondents), 17.72% are administrative staff (76 respondents) and 38.69% are students (166 respondents). As for educational qualifications, 40.79% hold a bachelor’s degree (175 respondents), 14.92% have a master’s degree (64 respondents), 22.14% hold a doctorate (PhD) (95 respondents) and 22.14% selected other qualifications (95 respondents). Faculty distribution includes 36.13% from science and engineering (155 respondents), 10.49% from business and management (45 respondents), 16.32% from social sciences and humanities (70 respondents), 16.32% from medicine and health sciences (70 respondents) and 20.75% from other faculties (89 respondents). In terms of transportation modes for commuting to campus, 34.50% use public transportation (148 respondents), 14.92% use private cars (64 respondents), 15.15% use bicycles (65 respondents), 22.14% walk (95 respondents), 6.29% carpool (27 respondents) and 6.99% use other modes (30 respondents). Finally, regarding sustainability or environmental workshops/courses, 62.94% have attended (270 respondents) while 37.06% have not (159 respondents).

To assess the potential presence of CMB in our study, we examined both the heterotrait–monotrait (HTMT) ratio and the inner variance inflation factor (VIF) values. Following the guidelines established by Nitzl (2016), we considered correlations exceeding r > 0.90 among constructs as indicative of possible CMB issues. Our analysis revealed that all correlations between constructs were well below this threshold, with the highest value in the HTMT table recorded at 0.712, indicating no significant CMB concerns. In addition, we evaluated the inner VIF values, as a VIF above 3.30 could suggest the presence of CMB within the model. Our structural model assessment indicated that the highest VIF observed was 2.477, which is comfortably below the recommended threshold of 3.30, as outlined by Kock (2015) and Adedeji et al. (2020). These results provide strong evidence that common method bias is not a concern in our data set, ensuring the validity of our research findings.

Table 1 presents the descriptive statistics and inter-relationships among the constructs, revealing significant correlations between all variables – SBT, IoTS, DARM, AF, DI, TA and GSC. Among the constructs, TA exhibited the highest mean value at 3.741, while IoTS had the lowest mean value at 3.519. These findings offer valuable insights into the perceptions of each construct and their relationships within the study.

The study used partial least squares structural equation modeling (PLS-SEM) using Smart-PLS 4.0, a widely recognized method for analyzing complex path models involving multiple variables. PLS-SEM is particularly suitable for models with numerous indicators or non-normally distributed data (Hair et al., 2020; Kock, 2015). Despite the data set being normally distributed, PLS-SEM was chosen over CB-SEM because of the model’s complexity and the flexibility required for examining variable relationships. Data preparation, including assessments for common method bias and linearity, was conducted using SPSS (version 26.0). Hypothesis testing and structural analysis were then performed with Smart-PLS 4.0, enabling a comprehensive evaluation of the constructs’ relationships and providing robust insights into the theoretical framework.

To rigorously assess the internal consistency and reliability of the variables, we used a comprehensive approach using both Cronbach’s alpha (CA) and Composite Reliability (CR), as recommended by Hair et al. (2021). As shown in Figure 2 and Table 2, the CA and CR values for all variables were well above the 0.7 threshold, confirming strong internal consistency and reliability. For convergent validity, we adhered to the criteria established by Hulland (1999) and Hair et al. (2021), which require factor loadings (FL) for each item to exceed 0.40 and the average variance extracted (AVE) to surpass 0.50. The results in Table 2 are highly favorable, with all items significantly exceeding the FL benchmark, showing loadings above 0.70. Additionally, each construct’s AVE values were greater than 0.50, indicating robust convergent validity. These findings affirm that the measurement model meets the recommended guidelines for internal consistency, reliability and convergent validity, as outlined by Hair et al. (2021).

To assess the discriminant validity of our measurements, we applied two well-established methods: the Fornell–Larcker criterion and the heterotrait–monotrait ratio (HTMT). We used the HTMT ratio. This method compares the correlations between different constructs with the correlations of items within the same construct. According to established guidelines, an HTMT value below 0.9 is considered acceptable (Henseler et al., 2015). Our analysis consistently showed that the correlation values among the latent variables were below this 0.9 threshold, as outlined by Henseler et al. (2015), and this is demonstrated in Table 3. These results provide robust evidence that the constructs in our model are distinct from one another, confirming adequate discriminant validity.

Following the comprehensive evaluation of the measurement model, our focus shifted to assessing collinearity within the structural model. This step involved a careful examination of key indicators, including the inner variance inflation factor (VIF), coefficient of determination (R²) and effect size (f²). The detailed results, confirm that all predefined thresholds for R², f² and inner VIF were satisfactorily met, indicating no significant collinearity concerns. With collinearity effectively addressed, we confidently proceeded to the in-depth testing of our hypotheses, as detailed in the subsequent section.

Table 4 and Figure 3 present the results of testing 12 hypotheses using bootstrapping with 10,000 resampling iterations. Each hypothesis examines the relationships between various model variables. The Beta (β) values indicate the strength and direction of these relationships, while the t-values and p-values assess statistical significance. For the mediation hypotheses, confidence intervals (lower limit [LL] and upper limit [UL]) are provided to confirm mediation effects. H1, which examines the relationship between SBT and GSC, is supported with a positive β = 0.089, a t-values of 2.174 (above the threshold of 1.96) and a p-value of 0.030 (below 0.05), confirming a significant positive relationship. H2, assessing the impact of IoTS on GSC, is also supported with β = 0.273, a t-values of 5.082 and a p-value of 0.000, indicating a strong positive effect. Similarly, H3, which evaluates the relationship between DARM and GSC, is significant, showing β = 0.371, a t-values of 4.578 and a p-value of 0.000, demonstrating a robust positive impact. H4, examining the effect of AF on GSC, is strongly supported with β = 0.220, a t-values of 3.312 and a p-value of 0.001, confirming a substantial positive effect. For the mediation hypotheses, H5 tests the mediation of TA between SBT and GSC and is supported with β = 0.033, a t-values of 1.977, a p-value of 0.048 and confidence intervals (LL = 0.006, UL = 0.103), confirming the mediation effect. H6, which assesses TA as a mediator between IoTS and GSC, is also supported with β = 0.052, a t-values of 2.953, a p-value of 0.041 and confidence intervals (LL = 0.013, UL = 0.123), indicating a significant mediation effect. Similarly, H7, which evaluates the mediating role of TA between DARM and GSC, is supported with β = 0.037, a t-values of 1.992, a p-value of 0.047 and confidence intervals (LL = 0.003, UL = 0.122), confirming mediation. H8, examining TA’s mediation between AF and GSC, is also supported with β = 0.049, a t-values of 2.596, a p-value of 0.046 and confidence intervals (LL = 0.007, UL = 0.135), validating the mediation effect. However, all these mediation effects are partial, as the direct relationships between the independent variables (IVs) and the dependent variable (DV) remain significant. Regarding moderation effects, H9 investigates the moderating role of DI between SBT and GSC and is found to be negatively significant, with β = −0.125, a t-values of 3.173 and a p-value of 0.002, suggesting that DI weakens this relationship. H10, assessing DI’s moderation between IoTS and GSC, is not supported, as indicated by β = −0.005, a t-values of 0.070 (below 1.96) and a p-value of 0.895 (above 0.05), showing that DI does not significantly moderate this relationship. H11, which evaluates DI as a moderator between DARM and GSC, is supported with β = 0.104, a t-values of 2.526 and a p-value of 0.012, suggesting that DI strengthens this relationship. H12, testing DI’s moderating effect between AF and GSC, is also supported with β = 0.089, a t-values of 2.023 and a p-value of 0.044, confirming a significant moderation effect. The lack of support for H10 may be because of DI not explaining sufficient variance in the relationship between IoTS and GSC, indicating that DI may not play a crucial moderating role in this specific interaction.

The findings of this study provide comprehensive insights into the role of technology integration in promoting GSC in Saudi Arabia, focusing on the impact of SBT, IoTS, DARM and AF. These findings align with the DOI, which emphasizes the importance of adoption processes and environmental factors in achieving the benefits of technological innovations. H1 confirmed that SBT significantly impacts GSC, underscoring its role in optimizing energy use, reducing carbon emissions and enhancing operational efficiency. This finding is consistent with previous studies emphasizing SBT’s advantages in energy efficiency and resource conservation (Al-Qahtani et al., 2024; Hassan et al., 2024a; 2024b; 2024c; 2024d). The DOI supports this result, as innovations with clear relative advantages and compatibility are more likely to be adopted (Rogers, 2003). In Saudi universities, SBT aligns with Vision 2030’s sustainability goals, making it a critical component for achieving sustainable campus outcomes. H2 demonstrated that IoTS significantly influences GSC, reflecting its role in real-time monitoring and resource optimization. This aligns with earlier findings that IoTS enhances energy efficiency and waste management in educational settings (Mahmoud et al., 2024). The DOI justifies this result by emphasizing that observable benefits and compatibility drive adoption. In the Saudi context, IoTS facilitates resource efficiency, aligning with Vision 2030’s digital transformation goals (Al-Adwani et al., 2024). H3 confirmed DARM’s positive impact on GSC by enabling data-driven decision-making for energy optimization and resource allocation. This supports existing literature on the benefits of analytics in sustainability (Khan et al., 2024). DOI highlights that perceived compatibility and visible outcomes encourage adoption, making DARM an essential tool for informed decision-making in Saudi universities striving for sustainability goals (Rogers, 2003). H4 revealed that AF significantly enhances GSC by streamlining energy management, waste processes and workflows. Previous research supports this finding, noting AF’s role in improving operational efficiency and reducing resource wastage (Hassan et al., 2024a; 2024b; 2024c; 2024d). DOI explains this result, as observable outcomes and reduced complexity drive innovation adoption. AF’s compatibility with existing infrastructure ensures its effectiveness in Saudi universities, contributing to Vision 2030’s sustainability objectives (Rahman and Al-Mutairi, 2024).

TA was found to significantly mediate the relationships between SBT, IoTS, DARM and AF with GSC (H5–H8). For SBT, TA enhances user acceptance and integration, maximizing its impact on sustainability (Al-Qahtani et al., 2024). Similarly, TA facilitates IoTS adoption by ensuring effective engagement and operational alignment, amplifying its sustainability benefits (Al-Mutairi et al., 2024). DARM’s adoption benefits from TA’s role in aligning analytics solutions with campus systems, improving resource management and decision-making (Khan et al., 2024). For AF, TA addresses complexity barriers, ensuring its integration and maximizing its operational benefits (Hassan et al., 2024a; 2024b; 2024c; 2024d). The DOI supports these findings by emphasizing that adoption processes, including training and engagement, are essential for realizing the full potential of innovations (Rogers, 2003).

DI significantly moderated the relationships between SBT, DARM and AF with GSC (H9, 11 and H12). For SBT, DI enhances energy system efficiency through seamless data integration and real-time monitoring (Ahmed et al., 2024a: 2024b). DARM benefits from robust DI in processing large data sets and optimizing resource strategies (Khan et al., 2024). Similarly, AF’s operational workflows and energy management are strengthened by advanced DI, supporting its sustainability impact (Al-Qahtani et al., 2024). DOI underscores the role of a conducive environment in amplifying innovation benefits, and DI’s role in these relationships aligns with this theoretical perspective (Rogers, 2003). Interestingly, DI did not significantly moderate the relationship between IoTS and GSC (H10). This finding diverges from prior research suggesting that DI is critical for IoTS (Mahmoud et al., 2024). However, it aligns with studies showing that IoTS, owing to its inherent flexibility and autonomous operation, can achieve sustainability outcomes even in basic digital environments (Hassan and Al-Fozan, 2024). DOI supports this by suggesting that innovations with strong relative advantages may be less reliant on external infrastructure (Rogers, 2003). This finding highlights IoTS’s adaptability, making it accessible for resource-constrained universities. Overall, the findings underscore the importance of both TA and DI in maximizing the impact of advanced technologies on GSC. TA ensures that innovations like SBT, IoTS, DARM and AF are effectively integrated, while DI provides the supportive environment needed for these technologies to thrive. The study reaffirms DOI’s relevance in understanding technology integration, emphasizing that innovation outcomes are shaped by both adoption dynamics and environmental factors. For Saudi universities, aligning technology investments with Vision 2030’s goals through strategic planning and cross-departmental collaboration is essential for achieving sustainability objectives.

The findings of this research provide substantial contributions to the theoretical understanding of technology integration and sustainability in higher education, particularly within Saudi Arabia and the broader global context. Grounded in the DOI, this study offers new insights into how digital innovations – including SBT, IoTS, DARM and AF – interact with TA and DI to influence GSC. This research extends DOI by exploring not only the direct adoption of technologies but also the mediating and moderating mechanisms that shape their sustainability impact. TA plays a critical mediating role, significantly influencing the relationships between SBT, IoTS, DARM and AF with GSC. The study highlights that effective adoption strategies, such as training, user engagement and integration planning, are essential for maximizing these technologies’ sustainability outcomes. Unlike prior studies that view adoption as a one-time event, this research positions TA as a dynamic process that sustains innovation effectiveness over time. DI, as a moderating factor, reveals nuanced insights. While DI significantly enhances the relationships of SBT, DARM and AF with GSC, it does not significantly influence IoTS. This suggests that technologies with greater adaptability, like IoTS, can function effectively even in resource-constrained environments, while SBT, DARM and AF require stronger digital infrastructure for optimal impact. These findings refine DOI by illustrating that external environmental factors do not influence all innovations uniformly; instead, their impact varies based on the technology’s characteristics and level of digital dependency. Beyond its application in Saudi Arabia’s higher education sector, this study aligns with global sustainability initiatives, including higher education efforts in North America, Europe and Asia. Universities worldwide have integrated AI-driven sustainability analytics, renewable energy systems and smart automation to enhance campus sustainability, further reinforcing the study’s theoretical contributions. By contextualizing DOI within Vision 2030’s digital transformation and sustainability goals, this research underscores DOI’s versatility in addressing diverse cultural, technological and organizational settings. Furthermore, this study highlights the interplay between technological attributes and organizational readiness, reaffirming DOI’s principles of relative advantage, compatibility and observability. It demonstrates that innovation diffusion is an iterative process, where adoption strategies, infrastructure development and sustainability objectives evolve together. Additionally, non-technological factors, such as institutional policies, leadership commitment and sustainability culture, also shape the success of technology adoption. By extending DOI, this research provides a refined theoretical framework for future studies on technology adoption, sustainability and digital infrastructure in higher education and other sectors, particularly in emerging economies. Future research should explore how AI-powered automation, renewable energy integration and blockchain-based sustainability tracking can further strengthen DOI’s application in fostering green and sustainable technological ecosystems across diverse industries.

The findings of this research provide practical guidance for higher education institutions in Saudi Arabia, particularly in achieving Vision 2030’s sustainability and digital transformation goals. The significant mediating role of TA in the relationships between SBT, IoTS, DARM and AF with GSC underscores the need for universities to prioritize effective adoption strategies. Comprehensive training programs, stakeholder engagement and ongoing technical support should be implemented to enhance user acceptance and maximize the sustainability impact of these technologies. The critical role of DI in moderating the effectiveness of SBT, DARM and AF on GSC highlights the importance of investing in robust infrastructure. Universities should upgrade digital capabilities, ensuring seamless connectivity, data integration and real-time analytics. Establishing digital policies that enhance network capacity, cybersecurity and data processing capabilities is vital to support advanced technologies. Interestingly, DI does not significantly moderate the relationship between IoTS and GSC, suggesting that IoTS can deliver sustainability benefits even with existing infrastructure. This allows universities, especially those with limited resources, to adopt IoTS incrementally while gradually improving their digital infrastructure. A holistic approach to technology integration is crucial, aligning investments with broader sustainability objectives. Cross-departmental collaboration and fostering a culture of innovation by engaging staff and students with new technologies can further enhance sustainability efforts.

In conclusion, universities must focus on both technology adoption strategies and digital infrastructure development to effectively leverage advanced technologies, achieving GSC and contributing to national sustainability goals under Vision 2030.

This study has three primary limitations. First, it focuses solely on Saudi Arabian universities, which may limit the generalizability of findings to regions with different technological infrastructure, regulatory policies and institutional priorities. Future research should explore cross-national comparisons to validate these results. Second, the study employs a cross-sectional design, capturing data at a single point in time, which may not reflect the evolving nature of technology adoption. Longitudinal studies are recommended to examine the long-term effects of technology-driven sustainability initiatives. Third, while this study focuses on SBT, IoTS, DARM and AF, future research should explore the role of emerging technologies such as artificial intelligence (AI) for predictive energy management and renewable energy systems for carbon neutrality. AI-driven automation can enhance resource optimization, while solar and wind energy solutions can reduce reliance on nonrenewable resources. Expanding research into other sectors, such as healthcare and public administration, could further enrich sustainability insights.

This study highlights the critical role of technology integration in fostering GSC in Saudi Arabia, focusing on SBT, IoTS, DARM and AF. Using the DOI as a foundation, the findings confirm that TA plays a key mediating role, amplifying the sustainability benefits of these technologies. Effective adoption strategies, including stakeholder engagement, training programs and policy support, are essential to maximize the impact of these innovations. Moreover, DI significantly moderates the relationships between SBT, DARM and AF with GSC, underscoring the necessity for substantial investments in digital infrastructure to ensure seamless technology integration. However, IoTS demonstrates greater adaptability, enabling sustainability benefits even in less-developed digital environments, which is particularly relevant for institutions with limited technological resources. These findings emphasize that universities must align technology investments with broader sustainability objectives, fostering collaboration across academic, administrative and operational units. Promoting a culture of innovation and strengthening technology engagement will further accelerate sustainability efforts, aligning with the ambitions of Saudi Vision 2030 and the United Nations Sustainable Development Goals (SDGs). Beyond its relevance to Saudi Arabia, this study contributes to the global discourse on sustainable campuses by drawing comparisons with successful implementations in North America, Europe and Asia. Universities worldwide have leveraged advanced technologies, circular economy strategies and AI-driven automation to enhance sustainability. Integrating these global insights with Saudi Arabia’s unique policy and institutional framework allows for a more comprehensive understanding of technology’s role in higher education sustainability. While this study is focused on Saudi universities, future research should explore cross-national case studies to compare technology adoption patterns and infrastructure challenges in diverse economic and cultural settings. Additionally, the long-term impacts of digital transformation on university sustainability should be examined using longitudinal research designs. Given the rapid evolution of emerging technologies such as AI, renewable energy systems and blockchain-based sustainability tracking, future studies should investigate their potential to complement and enhance current technology-driven sustainability strategies. In conclusion, this study reinforces the dual necessity of advanced technology adoption and infrastructure enhancement to achieve sustainable campus environments. By leveraging global best practices while addressing local challenges, universities can drive sustainability transformation, enhance institutional resilience and contribute meaningfully to both national and international sustainability agendas. The insights from this study provide a strategic roadmap for policymakers, university administrators and technology developers to align educational institutions with evolving sustainability demands and create a more resource-efficient, digitally enabled and environmentally conscious future.

The supplementary material for this article can be found online.